Data Engineering Roadmap, Learning Path,& Career Track 2024

Looking to become a successful data engineer? Check out ProjectPro's data engineering roadmap for 2024 to guide you through the skills and tools needed.

Data Engineering Roadmap, Learning Path,& Career Track 2024
 |  BY Manika

Data Engineering is gradually becoming a popular career option for young enthusiasts. However, with so many tools and technologies available, it can be challenging to know where to start. That's why we've created a comprehensive data engineering roadmap for 2023 to guide you through the essential skills and tools needed to become a successful data engineer. Let's dive into ProjectPro's Data Engineer Roadmap!

Furthermore, we will also lay out a learning path on becoming a data engineer that will help one explore this exciting domain. So, get set, go!


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Look at the image below and notice the exponential growth of data humans produce yearly. The graph shows that data is the future, and it is high time businesses start considering it as a helpful resource.

data engineer

Source:  Image uploaded by Tawfik Borgi on (researchgate.net)

So, what is the first step towards leveraging data? The first step is to work on cleaning it and eliminating the unwanted information in the dataset so that data analysts and data scientists can use it for analysis. That needs to be done because raw data is painful to read and work with. Making raw data more readable and accessible falls under the umbrella of a data engineer’s responsibilities. Thus, given that a data engineer is the first to interact with the data resource, anyone’s curiosity about pursuing a data engineer career path is justified. And for such curious beings, ProjectPro has prepared a blueprint to help beginners learn data engineering from scratch effortlessly. 

How to Become a Data Engineer With No Experience?

data engineer

If you have landed on this page, you will likely be looking for a data engineer roadmap that can list all the data engineering tools and guide you about a source for learning them. ProjectPro has precisely that in this section, but before presenting it, we would like to answer a few common questions to strengthen your inclination towards data engineering further.

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What is Data Engineering?

Data Engineering refers to creating practical designs for systems that can extract, keep, and inspect data at a large scale. It involves building pipelines that can fetch data from the source, transform it into a usable form, and analyze variables present in the data. These pipelines draw hidden insights about a business’s overall functioning and help stakeholders understand their customers, outreach, sales, etc.

Why do companies hire a Data Engineer?

In 2017, Gartner predicted that 85%of the data-based projects would fail and deliver the desired results. But, with companies gradually raising their investments in data infrastructures, the prediction is likely to turn out to be false. Along with that, the companies are likely to hire experts who can help them leverage data efficiently. And that is why the business managers look for data engineers, as they are the ones who will interact with the raw data, clean it, polish it, and make it analysis-ready. Data analysts and data scientists then use clean data to help stakeholders develop better business strategies.

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Data Engineer: Job Growth in Future

The demand for data engineers has been on a sharp rise since 2016. Years after that, we find a shortage in the number of skilled data engineers and an increase in the number of jobs. As per a 2020 report by DICE, data engineer is the fastest-growing job role and witnessed 50% annual growth in 2019. 

Data Engineer: Job Growth in Future

The report also mentioned that big tech giants like Amazon and Accenture are willing to dig a deep hole in their pockets for hiring skilled data engineers. And one can verify the fact that data engineers are among one of the highest-paid professionals if they take a look at the average salary of a data engineer. According to Indeed, the average salary of a data engineer in the US is $116,525 per year, and it is £40769 per year in the UK. The numbers are lucrative, and it is high time you start turning your dream of pursuing a data engineer career into reality.

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What do Data Engineers do?

Here are the responsibilities of a data engineer:

  • Serve as a data resource expert for the organization.

  • Build and execute data ETL solution pipelines for multiple clients in different industries.

  • Independently create data-driven solutions that are accurate and informative.

  • Interact with the data scientists team and assist them in providing suitable datasets for analysis.

  • Leverage various big data engineering tools and cloud service providing platforms to create data extractions and storage pipelines.

Data Engineering Requirements

Here is a list of skills needed to become a data engineer:

  • Highly skilled at graduation-level mathematics.

  • Good skills in computer programming languages like R, Python, Java, C++, etc.

  • High efficiency in advanced probability and statistics.

  • Ability to demonstrate expertise in database management systems.

  • Experience with using cloud services providing platforms like AWS/GCP/Azure.

  • Good knowledge of various machine learning and deep learning algorithms will be a bonus. 

  • Knowledge of popular big data tools like Apache Spark, Apache Hadoop, etc.

  • Good communication skills as a data engineer directly works with the different teams.

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Data Engineer Roadmap and Learning Path: Self-Taught

So, after reading the basic description of a data engineer, we hope you have decided to become a self-taught data engineer. We have prepared a how-to be data engineer roadmap for you. 

1. Computer Programming

A decent understanding and experience of a computer programming language is necessary for data engineering. And, considering how Python is becoming the most popular language (Statistics times), we suggest you start learning it if you haven’t already. Here is a book recommendation: Python for Absolute Beginners by Michael Dawson. The book is a fun read for an entry-level data engineer aspirant, and you won’t feel bored if you work on the exercises given in the book. It has an exciting way of introducing the readers to different variables and data types used in Python. Through fun challenges like word jumble game, tic tac toe game, pizza panic game, it explains strings, tuples, file handling, functions, Object-oriented programming, GUI, and animation in Python. You may skip chapters 11 and 12 as they are less useful for a database engineer.

2. Advanced Mathematics

By advanced mathematics, we mean that a data engineer should be good with vector calculus, differential equations, and linear algebra. As these mathematics topics are usually covered in most high school level textbooks, you don’t need to worry about learning them explicitly. However, for someone who wants to dive deeper, a book recommendation is Advanced Engineering Mathematics by Erwin Kreyszig. This book has detailed chapters that have been divided into eight parts. The first three parts (A, B, and C) will be enough for the mentioned topics. The book has many solved and unsolved problems, so make sure to go through them.

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3. Probability and statistics

When handling huge datasets, it is essential to look at various statistical parameters like mean, mode, median, etc., as they effectively summarise and label the data. Learning statistics becomes mandatory for a data engineer who has to work with large datasets. And suppose you are a budding data engineer who is new to the world of probability and statistics. In that case, we suggest you go through the textbook, Introduction to Mathematical Statistics by Robert Hogg, Joseph McKean, and Allan Craig. This is a popular book among graduate students for its beginner-friendly approach to laying the foundations of probability and statistics. After each sub-topic, the book has tons of solved examples and many unsolved exercises that one can practice alongside.

4. Database Management Systems

Softwares, called database management systems that assist in handling large datasets, are a part of data engineers’ everyday lives. These softwares allow editing and querying databases easily. Depending on the type of database a data engineer is working with, they will use specific software. Below, we mention a few popular databases and the different softwares used for them.

Type of Database

Softwares Used for Database Management

Relational Database

MySQL, IBM Db2, Oracle Database, Microsoft SQL Server, PostgreSQL

Graph Database

Neo4J, Datastax Enterprise Graph

Columnar Database

HBase, MariaDB, Cassandra,  Azure SQL Data Warehouse, Google BigQuery

NoSQL Database

Apache Cassandra, MongoDB, CouchBase,  CouchDB,

You don’t need to worry about learning all the DBMS mentioned above at once. Depending on the company you want to work with, you will be asked to learn them deeply. However, you may refer to Introduction to Database Systems by Korth, Silberschatz & Sudarshan for exploring things in brief.

5. Cloud Services Providers Platforms

As companies are gradually becoming more inclined towards investing in cloud computing for storing their data instead of bulky hardware systems, engineers who can work on cloud computing tools are in demand. The three most popular cloud service providing platforms are Google Cloud Platform, Amazon Web Services, and Microsoft Azure. All three platforms provide official certifications that one can pursue through official websites.

Learning Resources: How to Become a GCP Data Engineer 

How to Become a Azure Data Engineer

How to Become a Aws Data Engineer

6. Big Data Engineering Tools

The data size that a data engineer handles is usually large. To do that, a data engineer is likely to be expected to learn big data tools. These tools complement the knowledge of cloud computing as data engineers often implement codes that can handle large datasets over the cloud. Thus, having worked on projects that use tools like Apache Spark, Apache Hadoop, Apache Hive, etc., and their implementation on the cloud is a must for data engineers.

7. Machine Learning and Deep Learning

Understanding machine learning and deep learning algorithms aren’t a must for data engineers. However, as data engineers support the data scientist team, it will prove to be helpful if they learn ML and DL thoroughly. For machine learning, an introductory text by Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and for deep learning, the book by Ian Goodfellow and Yoshua Bengio and Aaron Courville will serve as a good reference. As a beginner, our suggestion is to not jump directly to deep learning and complete the machine learning book first.

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Learn Data Engineering through Practical Projects

After going through the resources mentioned in the previous section; one is not needed to pursue any of the data engineer courses that charge a hefty fee. You need a way to practice all that you have learned. Doing so will develop your skills and give you an idea of how data engineering tools are implemented in the real world. So, here is a list of projects that will rightly support your data engineering learning path.

Wine Quality Prediction: This data engineering project is a must for those interested in exploring the application of machine learning algorithms in Python. It is an easy project that beginners will find pretty helpful. It covers the details of different variables in the dataset and will teach you how to convert one data type into another in Python. Along with that, you will learn the basics of classification problems in machine learning and their application in predicting results.

Deep Learning Project for Beginners with Source Code: This project is a fun, beginner-friendly project for learning algorithms in deep learning. It will introduce all the basic blocks of a deep neural network: activation functions, feedforward network, backpropagation, loss function, and dropout regularization. The project will introduce deep learning libraries, including Tensorflow, Pytorch, Pytorch lightning, and Horovod.

Yelp Dataset Challenge Ideas- Analyse ratings from users: This project will allow you to explore different types of databases in the most practical way possible. You will learn different types of Databases like Hbase, Cassandra, Graph Databases and understand how to pick one for a given kind of database. Along with this, you will learn how to perform data analysis using GraphX and Neo4j.

Apache Zeppelin Demo Big Data Project for Data Analysis: This project is best for beginners exploring big data tools. It will introduce you to Apache Zeppelin and guide you to write Spark, Hive, and Pig code in notebooks.

No, No! The list does not end here. There are many Big Data tools that you can explore depending on the requirements of the business. Here are a few end-to-end solved projects for popular big data tools that you must check out:

Hadoop Projects

Hive Projects

Hbase Projects 

Apache Pig Projects 

HDFS Hadoop Projects

Oozie Example Projects

Spark Projects

We have a separate section for cloud service providing platforms that you can refer to below after you have completed the above projects.

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Azure Data Engineer Vs AWS Data Engineer Vs GCP Data Engineer

It might be difficult for an entry-level data engineer to pick one of the three popular cloud platforms; that is why we have prepared an easy table that you can refer to make an informed and quick decision.

Microsoft Azure

Amazon Web Services

Google Cloud Platform

  • Offers integration with Microsoft Windows.

  • Best suited for those looking for Platform-as-a-service (PaaS) provider.

  • Subscription plans are not so flexible.

  • It nicely supports Hybrid Cloud Space.

  • Yet to become a popular choice for big data technology.

  • Offers fun UI for the implementation of machine learning algorithms.

  • Best suited for those looking for  infrastructure-as-a-service (IaaS)

  • Popular among the open-source community members.

  • The more you use the product, the cheaper the subscription plans.

  • Support large-scale implementation of machine learning algorithms.

  • Supports big data technology well.

  • Supports high availability for data storage.

  • Supports uniform consistency of data throughout different locations.

  • Provides Google Developer console projects.

  • Similar pricing as AWS.

  • Support large-scale implementation of machine learning algorithms.

These are merely basic pointers that we have listed in brief. You must further explore AWS vs Azure and AWS vs GCP for a detailed analysis. After reading that, you are likely to conclude that as AWS was launched in 2002 and is usually considered the easiest to learn, it is the best option. However, make a note of other features as well when implementing cloud computing technology from a business perspective; a lot of different things have to be taken into consideration. 

AWS has the top-most share in the market, and that’s primarily because it was launched in 2002 while Microsoft and Google launched their cloud computing services in 2010 and 2009, respectively. And, that is why most beginners are instantly inclined towards exploring AWS. But, what if GCP and Azure are better choices for your organization? Given that it could be a possibility, we suggest you try out the following hands-on projects to understand the three services better and then decide.

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AWS Projects

AWS Project-Website Monitoring using AWS Lambda and Aurora

How to deal with slowly changing dimensions using Snowflake?

Building Real-Time AWS Log Analytics Solution

Snowflake Real-Time Data Warehouse Project for Beginners-1

AWS Snowflake Data Pipeline Example using Kinesis and Airflow

Orchestrate Redshift ETL using AWS Glue and Step Functions

Azure Projects

Analyze yelp reviews csv dataset project with spark parquet format

Azure Stream Analytics for Real-Time Cab Service Monitoring

Azure databricks tutorial project - Analysis of movielens dataset

GCP Projects

GCP Project-Build Pipeline using Dataflow Apache Beam Python 

Google Cloud - GCP Data Ingestion with SQL using Google Cloud Dataflow

GCP Project to Explore Cloud Functions using Python Part 1

Build a Scalable Event-Based GCP Data Pipeline using DataFlow

GCP Project to Learn using BigQuery for Exploring Data

FAQs on Data Engineer Job Role

How long does it take to become a data engineer?

If you have the correct data engineering learning path with you, you can easily become a data engineer in six months. All you have to do is work hard with utmost dedication on building those skills.

How much python should you know to become a data engineer?

Data Engineering with Python becomes easy, and that is why it has become a must for a data engineer. Python is relatively easy to learn, and practicing simple programs is usually enough for an aspiring data engineer. For starters, you should target learning different data types, file handling, and loops. After that, the more you work on industry projects, the better you will learn.

How to become a data engineer without a degree ?

Follow the learning path mentioned in this article to learn the basics of data engineering on your own and practise industry-relevant projects in the ProjectPro repository to become an expert in the domain.

How to become a data engineer from a BI developer?

The first step should be to hone the relevant skills a BI developer doesn’t have to become a data engineer. For appropriate resources, refer to this blog’s data engineering learning path. After that, work on enterprise-grade projects from the ProjectPro library to gain practical knowledge.

How to become a data engineer from being a data analyst?

A data analyst will easily comprehend the role of a data engineer. So, if they learn big data engineering tools and cloud computing, they should land a data engineer job easily. However, these two skills are best learned when working in the industry. Thus, we recommend you to check out the ProjectPro library and hone the two skills by working on their insightful projects.

 

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About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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